Biosensors provide an analysis of a biological fluid, such as whole blood, serum, plasma, urine, saliva, interstitial, or intracellular fluid. Typically, biosensors have a measurement device that analyzes a sample residing in a test sensor. The sample is typically in liquid form and in addition to being a biological fluid, may be the derivative of a biological fluid, such as an extract, a dilution, a filtrate, or a reconstituted precipitate. The analysis performed by the biosensor determines the presence and/or concentration of one or more analytes, such as alcohol, glucose, uric acid, lactate, cholesterol, bilirubin, free fatty acids, triglycerides, proteins, ketones, phenylalanine or enzymes, in the biological fluid. The analysis may be useful in the diagnosis and treatment of physiological abnormalities. For example, a diabetic individual may use a biosensor to determine the glucose level in whole blood for adjustments to diet and/or medication.
Many biosensors analyze for a single analyte and use various techniques to improve the accuracy and/or precision of the analysis. Accuracy may be expressed in terms of bias of the sensor system's analyte reading in comparison to a reference analyte reading, with larger bias values representing less accuracy, while precision may be expressed in terms of the spread or variance among multiple measurements. Calibration information may be used to improve the accuracy and/or precision of the analysis and may be read from the test sensor to the measurement device prior to the analysis. The measurement device uses the calibration information to adjust the analysis of the biological fluid in response to one or more parameters, such as the type of biological fluid, the particular analyte(s), and the manufacturing variations of the test sensor. Biosensors may be implemented using bench-top, portable, and like measurement devices. Portable measurement devices may be hand-held and allow for the identification and/or quantification of an analyte in a sample. Examples of portable measurement systems include the Ascensia Breeze® and Elite® meters of Bayer HealthCare in Tarrytown, N.Y., while examples of bench-top measurement systems include the Electrochemical Workstation available from CH Instruments in Austin, Tex.
The electrical signal input to the test sensor by the measurement device may be a potential or current and may be constant, variable, or a combination thereof, such as when an AC signal is applied with a DC signal offset. The input signal may be applied as a single pulse or in multiple pulses, sequences, or cycles. The analyte or a measurable species undergoes a redox reaction when the input signal is applied to the sample. The redox reaction generates the output signal that may be measured constantly or periodically during transient and/or steady-state output. Unlike a transient output signal that is changing, steady-state output is observed when the change of a signal with respect to its independent input variable (time, etc.) is substantially constant, such as within ±10 or ±5%.
Various electrochemical processes may be used such as coulometry, amperometry, voltammetry, or the like. Unlike coulometry, amperometry and voltammetry generally measure the rate at which the analyte is oxidized or reduced to determine the analyte concentration in the sample. In amperometry, an electrical signal of constant potential (voltage) is applied to the electrical conductors of the test sensor while the measured output signal is a current. In voltammetry, a varying potential is applied to a sample of biological fluid. Gated amperometry and gated voltammetry methods including alternating excitation and relaxation cycles also may be used.
The “hematocrit effect” is one factor that may reduce the accuracy and/or precision of an analysis performed in a whole blood sample. In addition to water, glucose, proteins, ketones, and other biological molecules, whole blood samples contain red blood cells. Hematocrit is the volume of a whole blood sample occupied by red blood cells in relation to the total volume of the whole blood sample and is often expressed as a percentage. The greater the hematocrit percentage deviates from the %-hematocrit system calibration for a whole blood sample, the greater the bias (error) in the analyte readings obtained from the biosensor. For example, a conventional biosensor system having one set of calibration constants (slope and intercept for the 40% hematocrit containing whole blood sample, for instance) will report three different glucose concentrations for whole blood samples having identical glucose concentrations, but hematocrit percentages of 20%, 40%, and 60%. Thus, even though the whole blood glucose concentrations are the same, the system will report that the 20% hematocrit whole blood sample contains more glucose than the 40% hematocrit whole blood sample, and that the 60% hematocrit whole blood sample contains less glucose than the 40% hematocrit whole blood sample. As conventional biosensors are generally configured to report glucose concentrations assuming a 40% hematocrit content for the whole blood sample, any glucose measurement performed on a blood sample containing less or more than 40% hematocrit will include some bias error attributable to the hematocrit effect.
Hematocrit bias may be expressed by the following equation:% Hct-Bias=100%×(Gm−Gref)/Gref,where Gm and Gref are the measured glucose and reference glucose readings, respectively, for any hematocrit level. The larger the absolute value of the %-Hct-bias, the larger the hematocrit effect.
In addition to the hematocrit effect, measurement inaccuracies also may arise when the measurable species concentration does not correlate with the analyte concentration. For example, when the biosensor determines the concentration of a reduced mediator generated in response to the oxidation of an analyte, any reduced mediator not generated by oxidation of the analyte will lead to an indication that more analyte is present in the sample than is correct due to mediator background.
By knowing the output signal attributable to factors not responsive to the concentration of the analyte, the spurious portion of the output signal may be subtracted. Conventional systems have attempted to isolate the non-responsive portions of the output signal by placing multiples pairs of working and counter electrodes in a common sample reservoir. By altering the reagents used to form the electrodes, these systems attempted to separate the analyte responsive and non-responsive portions by subtracting the two output signals.
For example, conventional sensor systems may have multiple detection areas in an undivided sample chamber, where each working electrode faces a reference electrode. In another aspect, these systems may have a single reference electrode. Systems of these types may provide an on-test sensor calibration system with two known standards or may provide separate electrode systems for analyte, interference, and hematocrit determination, for example. A disadvantage common to these systems is the single sample chamber, where the adjacent electrode systems/detection areas may be contaminated chemically from each other due to diffusion and/or liquid movement. This disadvantage may be especially problematic when one reagent system requires a longer assay time than another and/or when the test sensor is mechanically disturbed after filling with sample.
As more and more information regarding the analytes present in biological samples is necessary for diagnosis, there is an increasing need for routine monitoring of multiple biological species of medical importance. Accordingly, there is an ongoing need for improved biosensors, especially those that may provide increasingly accurate and/or precise concentration measurements for multiple analytes. The systems, devices, and methods of the present invention avoid or ameliorate at least one of the disadvantages associated with conventional biosensors.